Neural network-based PET image reconstruction.
In PET image analysis, conventional deconvolution alone will not give sufficient information for a precise study of a localized brain function. In the deconvolution process, which is a type of inverse problem, it is important to confine the solution space by incorporating a priori knowledge such as the tissue distribution given by MR images as well as smoothness in the blood flow distribution profile. An MR-embedded neural-network model is described to reduce the partial volume effect in the restoration of blood flow profiles from PET images.
Kosugi
Y
Y
Tokyo Institute of Technology, Yokohama, Japan. kosugi@pms.titech.ac.jp
Sase
M
M
Suganami
Y
Y
Uemoto
N
N
Momose
T
T
Nishikawa
J
J
D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D014055:Tomography, Emission-Computed